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Stock co-jump networks

Author

Listed:
  • Ding, Yi
  • Li, Yingying
  • Liu, Guoli
  • Zheng, Xinghua

Abstract

We propose a Degree-Corrected Block Model with Dependent Multivariate Poisson edges (DCBM-DMP) to study stock co-jump dependence. To estimate the community structure, we extend the SCORE algorithm in Jin (2015) and develop a Spectral Clustering On Ratios-of-Eigenvectors for networks with Dependent Multivariate Poisson edges (SCORE-DMP) algorithm. We prove that SCORE-DMP enjoys strong consistency in community detection. Empirically, using high-frequency data of S&P 500 constituents, we construct two co-jump networks according to whether the market jumps and find that they exhibit different community features than GICS. We further show that the co-jump networks help in stock return prediction.

Suggested Citation

  • Ding, Yi & Li, Yingying & Liu, Guoli & Zheng, Xinghua, 2024. "Stock co-jump networks," Journal of Econometrics, Elsevier, vol. 239(2).
    • Handle: RePEc:eee:econom:v:239:y:2024:i:2:s030440762300057x
    DOI: 10.1016/j.jeconom.2023.01.026
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    More about this item

    Keywords

    Network; Community detection; Jumps; Co-jumps; Stock dependence; High-frequency data;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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